np.split() for dividing arrays in NumPy - Mini Project: Build & Apply

Using np.split() to Divide Arrays

📖 Scenario: Imagine you have a list of daily temperatures for a week. You want to split this list into smaller parts to analyze each part separately.

🎯 Goal: You will create a NumPy array of temperatures, set a split index, use np.split() to divide the array, and then print the split parts.

📋 What You'll Learn

Create a NumPy array called temps with exact values: [22, 24, 19, 23, 25, 20, 21]

Create a variable called split_index with the value 3

Use np.split() with temps and [split_index] to split the array into two parts

Store the result in a variable called split_temps

Print the variable split_temps

💡 Why This Matters

🌍 Real World

Splitting data arrays helps analyze different sections separately, like dividing weekly sales data into weekdays and weekends.

💼 Career

Data scientists often split datasets to prepare for analysis, testing, or training machine learning models.

1

Create the NumPy array of temperatures

Import NumPy as np and create a NumPy array called temps with these exact values: [22, 24, 19, 23, 25, 20, 21]

NumPy

import numpy as np
# Create the NumPy array called temps with the exact values
# Your code here

Hint

Use np.array() to create the array with the exact list inside.

2

Set the split index

Create a variable called split_index and set it to the integer 3

NumPy

import numpy as np
temps = np.array([22, 24, 19, 23, 25, 20, 21])
# Create the variable split_index and set it to 3
# Your code here

Hint

Just assign the number 3 to the variable split_index.

3

Split the array using np.split()

Use np.split() with the array temps and the list [split_index] to split the array into two parts. Store the result in a variable called split_temps

NumPy

import numpy as np
temps = np.array([22, 24, 19, 23, 25, 20, 21])
split_index = 3
# Use np.split() to split temps at split_index and store in split_temps
# Your code here

Hint

Call np.split() with the array and a list containing split_index.

4

Print the split arrays

Print the variable split_temps to see the two parts of the array after splitting

NumPy

import numpy as np
temps = np.array([22, 24, 19, 23, 25, 20, 21])
split_index = 3
split_temps = np.split(temps, [split_index])
# Print the split_temps variable
# Your code here

Hint

Use print(split_temps) to display the result.

Practice

1. What does the np.split() function do in NumPy?

easy

A. It merges multiple arrays into one large array.

B. It divides an array into multiple smaller arrays at specified indices or into equal parts.

C. It sorts the elements of an array in ascending order.

D. It reshapes an array into a different shape without changing data.

Solution

1. Step 1: Understand the purpose of np.split()

   The function is designed to break a large array into smaller arrays either by specifying split points or by dividing into equal parts.

2. Step 2: Compare with other array functions

   Unlike merging, sorting, or reshaping, np.split specifically divides arrays.

3. Final Answer:

   It divides an array into multiple smaller arrays at specified indices or into equal parts. -> Option B

4. Quick Check:

   np.split() = divides array [OK]

Hint: Remember: split means break into parts [OK]

Common Mistakes:

• Confusing split with merge
• Thinking it sorts arrays
• Mixing with reshape

2. Which of the following is the correct syntax to split a 1D NumPy array arr into 3 equal parts using np.split()?

easy

A. np.split(arr, 3)

B. np.split(arr, [3])

C. np.split(arr, indices=3)

D. np.split(arr, sections=3)

Solution

1. Step 1: Recall np.split() syntax

   The function signature is np.split(array, indices_or_sections), where indices_or_sections can be an integer for equal parts.

2. Step 2: Identify correct usage

   Passing 3 as the second argument splits the array into 3 equal parts. Keyword arguments like 'indices_or_sections' are not used explicitly in common syntax.

3. Final Answer:

   np.split(arr, 3) -> Option A

4. Quick Check:

   np.split(arr, 3) splits into 3 parts [OK]

Hint: Use integer directly for equal parts [OK]

Common Mistakes:

• Using list instead of integer for equal parts
• Wrong keyword arguments
• Confusing indices with number of parts

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40, 50, 60])
splits = np.split(arr, [2, 4])
print(splits)

medium

A. [array([10]), array([20, 30, 40]), array([50, 60])]

B. [array([10, 20, 30]), array([40, 50, 60])]

C. [array([10, 20]), array([30, 40]), array([50, 60])]

D. [array([10, 20]), array([30]), array([40, 50, 60])]

Solution

1. Step 1: Understand split indices

   The list [2, 4] means split at index 2 and then at index 4.

2. Step 2: Divide the array accordingly

   arr[:2] = [10, 20], arr[2:4] = [30, 40], arr[4:] = [50, 60]

3. Final Answer:

   [array([10, 20]), array([30, 40]), array([50, 60])] -> Option C

4. Quick Check:

   Split at 2 and 4 gives 3 parts [OK]

Hint: Split indices mark where to cut array [OK]

Common Mistakes:

• Misunderstanding split points
• Off-by-one errors in slicing
• Confusing output format

4. The following code throws an error. What is the cause?

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
splits = np.split(arr, 3)
print(splits)

medium

A. The array length is not divisible by 3, so equal splits are impossible.

B. np.split() requires a list of indices, not an integer.

C. The array must be 2D to use np.split().

D. The print statement syntax is incorrect.

Solution

1. Step 1: Check array length and split parts

   The array length is 5, but we want to split into 3 equal parts.

2. Step 2: Understand np.split() behavior

   np.split() requires the array length to be divisible by the number of parts when splitting equally, otherwise it raises an error.

3. Final Answer:

   The array length is not divisible by 3, so equal splits are impossible. -> Option A

4. Quick Check:

   Length 5 not divisible by 3 causes error [OK]

Hint: Check divisibility of array length by parts [OK]

Common Mistakes:

• Using integer splits on incompatible length
• Confusing np.split with np.array_split
• Assuming print error

5. You have a 2D NumPy array data with shape (6, 4). You want to split it into 3 equal parts along the first axis (rows) for separate analysis. Which code correctly does this using np.split()?

hard

A. np.split(data, [1, 3, 5], axis=1)

B. np.split(data, 3, axis=1)

C. np.split(data, [2, 4], axis=1)

D. np.split(data, 3, axis=0)

Solution

1. Step 1: Understand array shape and axis

   data has 6 rows and 4 columns. Splitting into 3 equal parts along rows means each part has 2 rows.

2. Step 2: Use np.split() with correct axis and parts

   np.split(data, 3, axis=0) splits rows into 3 equal parts. axis=1 would split columns, which is not desired.

3. Final Answer:

   np.split(data, 3, axis=0) -> Option D

4. Quick Check:

   Split rows into 3 parts with axis=0 [OK]

Hint: Use axis=0 to split rows, axis=1 for columns [OK]

Common Mistakes:

• Splitting along wrong axis
• Using indices instead of number for equal parts
• Ignoring array shape